Chapter 10 Dominance-microbiome analysis

load("data/data.Rdata")
load("hmsc/fit_model1_250_10.Rdata")

10.0.1 Posterior estimates

The next step is to get the posterior estimates of beta, which is the parameter that links dominance with the microbiome. We employ a support threshold of ‘0.05’ to assign significance, meaning that parameters with posterior overlaps of <10% are considered significantly different.

# Select desired support threshold
support=0.9
negsupport=1-support

# Basal tree
postestimates_tree <- genome_tree %>%
        keep.tip(., tip=m$spNames) 

# Posterior estimate table
post_beta <- getPostEstimate(hM=m, parName="Beta")$support %>%
    as.data.frame() %>%
    mutate(variable=m$covNames) %>%
    pivot_longer(!variable, names_to = "genome", values_to = "value") %>%
    mutate(genome=factor(genome, levels=rev(postestimates_tree$tip.label))) %>%
    mutate(value = case_when(
          value >= support ~ "Positive",
          value <= negsupport ~ "Negative",
          TRUE ~ "Neutral")) %>%
    mutate(value=factor(value, levels=c("Positive","Neutral","Negative"))) %>%
    pivot_wider(names_from = variable, values_from = value) %>%
    rename(intercept=2) %>%
    select(genome,dominance,groupvariable) %>%
    column_to_rownames(var="genome")

# Aggregate basal GIFT into elements
function_table <- genome_gifts %>%
    to.elements(., GIFT_db) %>%
    as.data.frame()

#Phylums
phylum_colors <- read_tsv("https://raw.githubusercontent.com/earthhologenome/EHI_taxonomy_colour/main/ehi_phylum_colors.tsv") %>%
    right_join(genome_metadata, by=join_by(phylum == phylum)) %>%
    filter(genome %in% postestimates_tree$tip.label) %>%
    arrange(match(genome, postestimates_tree$tip.label)) %>%
    mutate(phylum = factor(phylum, levels = unique(phylum))) %>%
    column_to_rownames(var = "genome") %>%
    select(phylum)


colors_alphabetic <- read_tsv("https://raw.githubusercontent.com/earthhologenome/EHI_taxonomy_colour/main/ehi_phylum_colors.tsv") %>%
  right_join(genome_metadata, by=join_by(phylum == phylum)) %>%
    filter(genome %in% postestimates_tree$tip.label) %>%
    arrange(match(genome, postestimates_tree$tip.label)) %>%
     select(phylum, colors) %>%
    unique() %>%
    arrange(phylum) %>%
    select(colors) %>%
    pull()

# Basal ggtree
postestimates_tree <- postestimates_tree %>%
        force.ultrametric(.,method="extend") %>%
        ggtree(., size = 0.3)
***************************************************************
*                          Note:                              *
*    force.ultrametric does not include a formal method to    *
*    ultrametricize a tree & should only be used to coerce    *
*   a phylogeny that fails is.ultrametric due to rounding --  *
*    not as a substitute for formal rate-smoothing methods.   *
***************************************************************
#Add phylum colors next to the tree tips
postestimates_tree <- gheatmap(postestimates_tree, phylum_colors, offset=-0.2, width=0.1, colnames=FALSE) +
      scale_fill_manual(values=colors_alphabetic)+
      labs(fill="Phylum")

#Reset fill scale to use a different colour profile in the heatmap
postestimates_tree <- postestimates_tree + new_scale_fill()

# Add posterior significant heatmap
postestimates_tree <- gheatmap(postestimates_tree, post_beta, offset=0, width=0.2, colnames=TRUE, colnames_position="top",colnames_angle=90, colnames_offset_y=1, hjust=0) +
        scale_fill_manual(values=c("#be3e2b","#f4f4f4","#b2b530"))+
        labs(fill="Trend")

#Reset fill scale to use a different colour profile in the heatmap
postestimates_tree <- postestimates_tree + new_scale_fill()

#Add functions heatmap
postestimates_tree <- gheatmap(postestimates_tree, function_table, offset=0.6, width=3.5, colnames=FALSE) +
            vexpand(.08) +
            coord_cartesian(clip = "off") +
            scale_fill_gradient(low = "#f4f4f4", high = "steelblue", na.value="white") +
            labs(fill="Function fullness")

#Reset fill scale to use a different colour profile in the heatmap
postestimates_tree <- postestimates_tree + new_scale_fill()

# Add completeness barplots
postestimates_tree <- postestimates_tree +
            geom_fruit(data=genome_metadata,
            geom=geom_bar,
            grid.params=list(axis="x", text.size=2, nbreak = 1),
            axis.params=list(vline=TRUE),
            mapping = aes(x=length, y=genome, fill=completeness),
                 offset = 3.85,
                 orientation="y",
                 stat="identity") +
            scale_fill_gradient(low = "#cf8888", high = "#a2cc87") +
            labs(fill="Genome completeness")

postestimates_tree +
        vexpand(.25, 1) # expand top

### Positively associated genomes

getPostEstimate(hM=m, parName="Beta")$support %>%
    as.data.frame() %>%
    mutate(variable=m$covNames) %>%
    pivot_longer(!variable, names_to = "genome", values_to = "value") %>%
    mutate(trend = case_when(
          value >= support ~ "Positive",
          value <= negsupport ~ "Negative",
          TRUE ~ "Neutral")) %>%
    filter(variable=="dominance", trend=="Positive") %>%
    arrange(-value) %>%
    left_join(genome_metadata,by=join_by(genome==genome)) %>%
    select(genome,phylum,class,order,species,value) %>%
    tt()
tinytable_iamhfbcbacrjz7yuw8fk
genome phylum class order species value
A_DRC05_bin.59 p__Bacteroidota c__Bacteroidia o__Bacteroidales s__Duncaniella sp910577345 1.000
C_HT_bin.102 p__Bacillota c__Bacilli o__Lactobacillales s__Limosilactobacillus reuteri 0.998
A_HTC12_bin.48 p__Bacillota c__Bacilli o__Lactobacillales s__Lactobacillus johnsonii 0.995
C_HT_bin.9 p__Bacillota_A c__Clostridia o__Oscillospirales s__QXXE01 sp910589115 0.994
A_DTC03_bin.77 p__Bacteroidota c__Bacteroidia o__Bacteroidales s__CAG-873 sp910586975 0.992
B_C04M3_bin.80 p__Bacillota_A c__Clostridia o__Lachnospirales s__ 0.991
B_C05M2_bin.12 p__Bacteroidota c__Bacteroidia o__Bacteroidales s__CAG-873 sp910578095 0.991
A_CDC10_bin.30 p__Bacteroidota c__Bacteroidia o__Bacteroidales s__Duncaniella sp910589485 0.990
B_C03M5_bin.33 p__Bacillota_B c__Dehalobacteriia o__UBA4068 s__ 0.990
A_CDC13_bin.65 p__Bacillota_A c__Clostridia o__Lachnospirales s__Eubacterium_J sp009774535 0.987
A_T3C12_bin.34 p__Bacteroidota c__Bacteroidia o__Bacteroidales s__CAG-485 sp009775355 0.985
C_DR_bin.56 p__Bacteroidota c__Bacteroidia o__Bacteroidales s__CAG-485 sp910578075 0.985
B_C04M3_bin.108 p__Bacillota_A c__Clostridia o__Oscillospirales s__Acutalibacter sp009936035 0.984
B_C06M3_bin.61 p__Bacillota c__Bacilli o__Erysipelotrichales s__C-19 sp910576855 0.981
B_C12F1_bin.72 p__Bacteroidota c__Bacteroidia o__Bacteroidales s__Duncaniella sp910578515 0.980
C_HR_bin.153 p__Bacillota_A c__Clostridia o__Oscillospirales s__ 0.980
A_T1C10_bin.3 p__Bacteroidota c__Bacteroidia o__Bacteroidales s__UBA7173 sp002491305 0.979
B_C11F5_bin.68 p__Bacteroidota c__Bacteroidia o__Bacteroidales s__UBA7173 sp001689485 0.979
B_C12F1_bin.61 p__Bacteroidota c__Bacteroidia o__Bacteroidales s__CAG-873 sp009775535 0.977
A_DRC04_bin.49 p__Bacillota_A c__Clostridia o__Oscillospirales s__Enterenecus sp910587255 0.975
C_DR_bin.4 p__Bacillota_A c__Clostridia o__Oscillospirales s__Avispirillum sp011957885 0.974
A_HTC03_bin.32 p__Bacillota_A c__Clostridia o__Oscillospirales s__Pelethomonas sp910587645 0.973
B_C04M5_bin.101 p__Bacteroidota c__Bacteroidia o__Bacteroidales s__Paramuribaculum sp910579675 0.973
B_C11F3_bin.29 p__Bacillota_A c__Clostridia o__Lachnospirales s__CAG-95 sp009911035 0.973
A_CDC12_bin.65 p__Bacillota_A c__Clostridia o__Christensenellales s__Coproplasma sp003979335 0.972
B_C13F3_bin.77 p__Bacteroidota c__Bacteroidia o__Bacteroidales s__CAG-873 sp910587915 0.972
A_CDC13_bin.13 p__Bacillota_A c__Clostridia o__Lachnospirales s__UBA3402 sp002358555 0.971
B_C05M3_bin.38 p__Bacillota_A c__Clostridia o__Lachnospirales s__Eubacterium_J sp910574915 0.971
B_C13F3_bin.53 p__Bacillota c__Bacilli o__Erysipelotrichales s__Erysipelatoclostridium cocleatum 0.970
A_DTC13_bin.55 p__Bacillota_A c__Clostridia o__Christensenellales s__RACS-045 sp910579785 0.969
A_T2C12_bin.11 p__Bacillota_A c__Clostridia o__Oscillospirales s__NSJ-51 sp910585415 0.969
B_C06M5_bin.62 p__Bacillota_A c__Clostridia o__Christensenellales s__CAG-475 sp910577815 0.968
B_C13F3_bin.5 p__Bacillota_A c__Clostridia o__TANB77 s__ 0.967
B_C13F3_bin.97 p__Bacillota_A c__Clostridia o__TANB77 s__ 0.967
C_CR_bin.18 p__Bacillota_A c__Clostridia o__Lachnospirales s__VSOB01 sp910589075 0.966
C_DT_bin.159 p__Bacteroidota c__Bacteroidia o__Bacteroidales s__ 0.965
A_T2C05_bin.3 p__Bacteroidota c__Bacteroidia o__Bacteroidales s__Muribaculum gordoncarteri 0.964
B_C13F2_bin.43 p__Bacillota_A c__Clostridia o__Oscillospirales s__Enterenecus sp910587255 0.963
B_C04M3_bin.128 p__Bacillota_A c__Clostridia o__Oscillospirales s__Anaerotruncus sp000403395 0.962
B_C13F3_bin.35 p__Bacillota_A c__Clostridia o__Lachnospirales s__MGBC136627 sp910585935 0.961
A_OPC13_bin.50 p__Bacillota_A c__Clostridia o__Oscillospirales s__UBA1405 sp910589145 0.960
B_C05M1_bin.75 p__Bacillota_A c__Clostridia o__Lachnospirales s__Clostridium_Q sp009911305 0.959
B_C13F3_bin.70 p__Bacillota_A c__Clostridia o__Oscillospirales s__ 0.959
A_CDC05_bin.89 p__Bacteroidota c__Bacteroidia o__Bacteroidales s__ 0.958
A_OPC10_bin.79 p__Bacillota_A c__Clostridia o__Lachnospirales s__UBA3402 sp910574345 0.957
C_OP_bin.44 p__Bacillota_A c__Clostridia o__Lachnospirales s__Ventrimonas sp910586205 0.957
C_HT_bin.137 p__Bacillota_A c__Clostridia o__Oscillospirales s__ 0.956
C_CR_bin.157 p__Bacillota_A c__Clostridia o__Christensenellales s__ 0.954
C_T3_bin.64 p__Bacillota_A c__Clostridia o__Oscillospirales s__NSJ-51 sp910585415 0.954
C_DR_bin.31 p__Bacillota_A c__Clostridia o__Lachnospirales s__Anaerotignum sp910576545 0.953
A_OPC10_bin.40 p__Bacillota_A c__Clostridia o__Oscillospirales s__ 0.952
B_C12F2_bin.47 p__Bacillota c__Bacilli o__Lactobacillales s__Enterococcus_D casseliflavus 0.952
C_HR_bin.13 p__Bacillota_A c__Clostridia o__Oscillospirales s__Evtepia sp910584805 0.952
C_T2_bin.55 p__Bacillota_A c__Clostridia o__Lachnospirales s__ 0.952
B_C04M2_bin.33 p__Bacillota_A c__Clostridia o__Oscillospirales s__Enterenecus sp910585265 0.951
B_C04M3_bin.31 p__Bacillota_A c__Clostridia o__Oscillospirales s__Angelakisella sp013316495 0.951
A_OPC03_bin.64 p__Bacillota_A c__Clostridia o__Lachnospirales s__Acetatifactor sp910589655 0.950
A_T1C11_bin.9 p__Bacteroidota c__Bacteroidia o__Bacteroidales s__CAG-485 sp002362485 0.949
B_C04M3_bin.18 p__Bacillota_A c__Clostridia o__Lachnospirales s__Eubacterium_J sp910586445 0.949
B_C03M3_bin.127 p__Bacillota_A c__Clostridia o__Oscillospirales s__Acutalibacter sp009936035 0.948
B_C04M4_bin.45 p__Bacillota_A c__Clostridia o__Oscillospirales s__ 0.948
A_T3C03_bin.17 p__Bacillota_A c__Clostridia o__Christensenellales s__QANA01 sp910588425 0.947
B_C04M2_bin.76 p__Bacillota_A c__Clostridia o__Lachnospirales s__CAG-56 sp004793585 0.945
A_HRC11_bin.35 p__Bacillota_A c__Clostridia o__Oscillospirales s__ 0.944
B_C04M4_bin.5 p__Bacillota_A c__Clostridia o__Lachnospirales s__Choladocola sp009774145 0.944
C_DT_bin.37 p__Bacillota_A c__Clostridia o__Oscillospirales s__Acutalibacter sp009936035 0.944
C_DR_bin.21 p__Bacillota_A c__Clostridia o__Lachnospirales s__MD308 sp910578455 0.943
A_CDC03_bin.78 p__Bacillota_A c__Clostridia o__Oscillospirales s__Lawsonibacter sp910577805 0.942
B_C13F5_bin.28 p__Bacteroidota c__Bacteroidia o__Bacteroidales s__Duncaniella sp910576785 0.942
A_HRC10_bin.28 p__Bacillota_A c__Clostridia o__Christensenellales s__Gallimonas sp910578065 0.941
A_T3C11_bin.23 p__Bacillota_A c__Clostridia o__Lachnospirales s__RGIG4284 sp910576205 0.941
A_HTC12_bin.21 p__Bacillota_A c__Clostridia o__Lachnospirales s__UBA3402 sp910575585 0.940
A_T2C05_bin.28 p__Bacillota c__Bacilli o__Erysipelotrichales s__MGBC163490 sp910588075 0.940
B_C10F2_bin.5 p__Bacillota_A c__Clostridia o__Oscillospirales s__ 0.940
C_HR_bin.146 p__Bacillota_A c__Clostridia o__Lachnospirales s__Choladocola sp910575445 0.940
B_C05M5_bin.57 p__Bacillota_A c__Clostridia o__Oscillospirales s__Pelethomonas sp910587785 0.939
B_C10F5_bin.45 p__Bacillota_A c__Clostridia o__Oscillospirales s__Dysosmobacter sp000403435 0.939
B_C12F3_bin.66 p__Bacteroidota c__Bacteroidia o__Bacteroidales s__ 0.939
B_C11F5_bin.87 p__Bacillota_A c__Clostridia o__Lachnospirales s__ 0.938
A_CRC05_bin.32 p__Bacillota_A c__Clostridia o__Lachnospirales s__Eubacterium_J sp910579075 0.937
B_C04M1_bin.34 p__Bacillota_A c__Clostridia o__Oscillospirales s__Pelethomonas sp910579965 0.937
B_C04M3_bin.90 p__Bacillota_A c__Clostridia o__Lachnospirales s__14-2 sp910580015 0.937
C_T2_bin.30 p__Bacillota_A c__Clostridia o__Lachnospirales s__ 0.934
C_T3_bin.41 p__Bacillota_A c__Clostridia o__Lachnospirales s__Acetatifactor sp910584235 0.934
C_CD_bin.124 p__Bacillota_A c__Clostridia o__Oscillospirales s__Pelethomonas sp910579965 0.933
C_OP_bin.73 p__Bacillota_A c__Clostridia o__Lachnospirales s__UBA3402 sp910585435 0.933
C_HR_bin.50 p__Bacillota_A c__Clostridia o__Lachnospirales s__UBA3402 sp910588325 0.932
A_CDC03_bin.64 p__Bacillota_A c__Clostridia o__Lachnospirales s__ 0.930
B_C03M3_bin.121 p__Bacillota_A c__Clostridia o__Lachnospirales s__Lachnoclostridium_B sp910574185 0.930
B_C10F1_bin.59 p__Bacillota_A c__Clostridia o__Lachnospirales s__Choladocola sp910583895 0.929
B_C10F5_bin.36 p__Bacillota_A c__Clostridia o__Lachnospirales s__UBA3282 sp003611805 0.928
B_C11F5_bin.21 p__Bacillota_A c__Clostridia o__Lachnospirales s__COE1 sp000403335 0.928
A_CDC10_bin.13 p__Bacillota_A c__Clostridia o__Christensenellales s__MGBC113161 sp910587565 0.927
A_DRC13_bin.41 p__Bacillota_A c__Clostridia o__Oscillospirales s__Acutalibacter muris 0.927
C_CD_bin.62 p__Bacillota_A c__Clostridia o__Lachnospirales s__1XD42-69 sp009911505 0.925
C_OP_bin.15 p__Bacillota_A c__Clostridia o__Lachnospirales s__Roseburia sp910584215 0.925
A_CDC05_bin.91 p__Bacillota_A c__Clostridia o__Oscillospirales s__ 0.923
C_T2_bin.92 p__Bacillota_A c__Clostridia o__TANB77 s__ 0.923
A_CDC05_bin.30 p__Bacillota_A c__Clostridia o__Oscillospirales s__ 0.922
A_CRC04_bin.71 p__Bacillota_A c__Clostridia o__Lachnospirales s__Sporofaciens sp910574885 0.922
B_C13F3_bin.99 p__Bacillota_A c__Clostridia o__Lachnospirales s__Merdisoma sp910574255 0.922
B_C04M1_bin.8 p__Bacillota_A c__Clostridia o__Lachnospirales s__Merdisoma sp910576325 0.921
B_C03M2_bin.44 p__Bacteroidota c__Bacteroidia o__Bacteroidales s__Duncaniella muris 0.920
B_C06M5_bin.16 p__Actinomycetota c__Coriobacteriia o__Coriobacteriales s__Adlercreutzia muris 0.920
C_T3_bin.99 p__Bacillota_A c__Clostridia o__Oscillospirales s__Pelethomonas sp910577915 0.920
B_C05M1_bin.6 p__Bacillota_A c__Clostridia o__Lachnospirales s__Ventrimonas sp910584845 0.919
A_DRC06_bin.37 p__Bacteroidota c__Bacteroidia o__Bacteroidales s__CAG-873 sp009775265 0.918
B_C12F1_bin.38 p__Bacillota_A c__Clostridia o__Oscillospirales s__Dysosmobacter sp910588005 0.918
B_C10F1_bin.10 p__Bacillota_A c__Clostridia o__Lachnospirales s__Ventrimonas sp910577765 0.917
B_C13F3_bin.93 p__Bacillota_A c__Clostridia o__Oscillospirales s__Anaerotruncus sp003612625 0.917
A_HRC05_bin.71 p__Bacillota_A c__Clostridia o__Christensenellales s__MGBC100320 sp910577995 0.915
A_T3C13_bin.3 p__Bacillota_A c__Clostridia o__Oscillospirales s__ 0.914
B_C03M1_bin.60 p__Bacillota_A c__Clostridia o__Christensenellales s__MGBC100798 sp910584975 0.911
C_OP_bin.100 p__Bacillota_A c__Clostridia o__Lachnospirales s__ 0.910
C_DT_bin.155 p__Bacillota_A c__Clostridia o__Lachnospirales s__ 0.909
A_T1C03_bin.24 p__Bacillota_A c__Clostridia o__Lachnospirales s__UMGS1370 sp910577645 0.907
B_C12F1_bin.23 p__Bacillota_A c__Clostridia o__Oscillospirales s__Pelethomonas sp910579965 0.907
A_T2C13_bin.14 p__Bacteroidota c__Bacteroidia o__Bacteroidales s__UBA7173 sp001689685 0.906
A_CRC05_bin.73 p__Bacillota_A c__Clostridia o__Christensenellales s__MGBC100320 sp910588305 0.905
B_C03M1_bin.29 p__Bacillota_A c__Clostridia o__Lachnospirales s__Ventrimonas sp009911065 0.905
B_C04M5_bin.14 p__Actinomycetota c__Coriobacteriia o__Coriobacteriales s__Adlercreutzia caecimuris 0.905
C_HR_bin.28 p__Bacillota_A c__Clostridia o__Lachnospirales s__Sporofaciens sp910585725 0.903
B_C04M5_bin.93 p__Bacillota_A c__Clostridia o__Christensenellales s__Gallimonas sp910588465 0.902

10.0.2 Negatively associated genomes

getPostEstimate(hM=m, parName="Beta")$support %>%
    as.data.frame() %>%
    mutate(variable=m$covNames) %>%
    pivot_longer(!variable, names_to = "genome", values_to = "value") %>%
    mutate(trend = case_when(
          value >= support ~ "Positive",
          value <= negsupport ~ "Negative",
          TRUE ~ "Neutral")) %>%
    filter(variable=="dominance", trend=="Negative") %>%
    arrange(value) %>%
    left_join(genome_metadata,by=join_by(genome==genome)) %>%
    select(genome,phylum,class,order,species,value) %>%
    tt()
tinytable_5x1r38xihzc30pe4jwl8
genome phylum class order species value
A_T3C05_bin.41 p__Pseudomonadota c__Gammaproteobacteria o__Burkholderiales s__Parasutterella excrementihominis 0.095

10.1 Predict responses to dominance

Based on the fitted model, we can predict the structure of the microbial community for each level of dominance for a certain value of sequencing depth.

# Select modelchain of interest
load("hmsc/fit_model1_250_10.Rdata")

gradient = seq(0,1,0.1)
gradientlength = length(gradient)

#Treatment-specific gradient predictions
pred <- constructGradient(m, 
                      focalVariable = "dominance", 
                      non.focalVariables = list(logseqdepth=list(1)), 
                      ngrid=gradientlength) %>%
             predict(m, Gradient = ., expected = TRUE)
# weights:  3 (2 variable)
initial  value 162.196440 
final  value 133.205769 
converged
predY <- pred %>%
        do.call(rbind,.) %>%
        as.data.frame() %>%
        mutate(dominance=rep(gradient,1000)) %>%
        pivot_longer(-c(dominance), names_to = "genome", values_to = "value")

10.1.1 Plot responses to dominance

The estimated responses of genomes exhibiting significant positive and negative association with dominance.

#Get phylum colors from the EHI standard
phylum_colors <- genome_metadata %>%
    left_join(read_tsv("https://raw.githubusercontent.com/earthhologenome/EHI_taxonomy_colour/main/ehi_phylum_colors.tsv"), by=join_by(phylum == phylum)) %>%
    arrange(match(genome, genome_tree$tip.label)) %>%
    select(phylum, colors) %>%
    unique() %>%
    arrange(phylum) %>%
    slice(2:5) %>%
    select(colors) %>%
    pull()

getPostEstimate(hM=m, parName="Beta")$support %>%
    as.data.frame() %>%
    mutate(variable=m$covNames) %>%
    pivot_longer(!variable, names_to = "genome", values_to = "value") %>%
    mutate(trend = case_when(
          value >= support ~ "Positive",
          value <= negsupport ~ "Negative",
          TRUE ~ "Neutral")) %>%
    filter(variable=="dominance", trend!="Neutral") %>%
    select(genome,trend) %>%
    left_join(predY, by=join_by(genome==genome)) %>%
    filter(trend != "Neutral") %>%
    #filter(genome %in% predY_asc)  %>% #only display genomes with contrasting dynamics across treatments
    group_by(genome, trend, dominance) %>%
    summarize(value = mean(value, na.rm = TRUE)) %>%
    left_join(genome_metadata, by=join_by(genome == genome)) %>%
    ggplot(aes(x=dominance, y=value, group=genome, color=phylum)) + 
        geom_line() +
        scale_color_manual(values=phylum_colors) +
        facet_grid(fct_rev(trend) ~ phylum) +
        labs(y="Genome abundance (log)",x="Dominance") +
        theme(legend.position = "none") +
        theme_minimal() +
         theme(legend.position = "none",
               axis.text.x = element_text(angle = 45, hjust = 0.8,),
               axis.line.x = element_line(size = 0.5, linetype = "solid", colour = "black"),
)